Convergent Alterations of a Protein Hub Produce Divergent Effects within a Binding Site

Progress in tumor sequencing and cancer databases has created an enormous amount of information that scientists struggle to sift through. While several research groups have created computational methods to analyze these databases, much work still remains in distinguishing key implications of pathogenic mutations. Here, we describe an approach to identify and evaluate somatic cancer mutations of WD40 repeat protein 5 (WDR5), a chromatin-associated protein hub. This multitasking protein maintains the functional integrity of large multi-subunit enzymatic complexes of the six human SET1 methyltransferases. Remarkably, the somatic cancer mutations of WDR5 preferentially distribute within and around an essential cavity, which hosts the WDR5 interaction (Win) binding site. Hence, we assessed the real-time binding kinetics of the interactions of key clustered WDR5 mutants with the Win motif peptide ligands of the SET1 family members (SET1Win). Our measurements highlight that this subset of mutants exhibits divergent perturbations in the kinetics and strength of interactions not only relative to those of the native WDR5 but also among various SET1Win ligands. These outcomes could form a fundamental basis for future drug discovery and other developments in medical biotechnology.


Location of somatic cancer mutations from tumor samples with N < 500 and their distributions with a well-defined upper limit of mutations (Supplementary
11. Equilibrium dissociation constants of WDR5 mutants with SET1Win ligands using BLI measurements (Supplementary Table S9).
12. Structural information on the effect of the S175L mutation (Supplementary Fig. S5). 13. Steady-state FP spectroscopy curves for the interactions of WDR5 mutants with SET1win ligands (Supplementary Fig. S6).
14. Very weak interactions of D92N with SET1Win ligands are detected by steady-state FP spectroscopy measurements (Supplementary Fig. S7).

SET1Win
P -7 P -6 P -5 P -4 P -3 P -2 P -1 P 0 P 1 P 2 P 3 P 4 P 5 P 6 Charge S * This is an R3771S-substituted MLL1Win peptide ligand. S ** This is an R2517S-substituted MLL4Win peptide ligand. Supplementary Table S2. Location of mutations from tumor samples with N < 500. The mutation distribution from tumor samples with low N values is shown below. Out of 8 such somatic cancer mutations, 5 were within or around the Win binding site, while 3 were found elsewhere. Mutated residues are either inside the WDR5 cavity ( ■ ), have established interactions with SET1Win ( □ ), or sequentially are one residue away from residues with established interactions with SET1Win ( ○ ). 1,4 Mutations within and around the Win binding site

Independent Mutations
S-4 Supplementary Table S3. Distributions of somatic cancer mutations in tumors with a well-defined upper limit of mutations. Diverse mutation clusters correspond to different maximum number of mutations, Nmax. The mutations highlighted in yellow were studied further using BLI and steady-state FP spectroscopy. S218F and D92N mutations, which are located within the WDR5 cavity, were found in tumors with N > 10,000. For compiling these mutations, the COSMIC database was used. [5][6][7] Condition Mutations N < 10,000 S-5

Results of mutation clustering for different Nmax-based mutation subsets
Supplementary Fig. S1. Results of mutation clustering for different Nmax-based mutation subsets. (A) WAP scores were calculated using 4 different subsets of mutations divided on the basis of the genetic damage, N, in their corresponding tumors. The P-values were calculated by comparing to the calculated WAP scores to those corresponding to random permutations of the mutation distribution.  *F133L and S175L also meet the clustering with N < 500 condition. It was found that F133L disrupts the mitotic progression in the cell cycle process. 8 **S91F was not studied experimentally. It does not meet the clustering with the N < 500 condition, yet it can disrupt known Win site interactions (Supplementary Table S5). For example, a related mutant, S91K, is not able to make interactions with a minimal C-terminal SET catalytic domain of MLL1. 9 ***D172A and D92N were experimentally studied using single-molecule electrical recordings and an MLL4Win-containing engineered nanopore. 3 In addition, D172A was recently studied using pull-down assays, showing declined interactions with histone H3 peptides with respect to the native WDR5 protein. 10

List of noncovalent bonds at the WDR5-SET1Win protein interface
Supplementary Table S5. Mapping of hydrogen bonds at the WDR5-SET1Win interface. These results were obtained using previously published co-crystallization data of Dharmarajan and co-workers. 1 The cut-off distance for identifying these hydrogen bonds was 4.0 Å. Here, BB and SC denote backbone and side chain, respectively. These interactions were determined using protein interactions calculator (PIC). 11 The structures were not always able to model the whole sequence of the peptides, so the list of these hydrogen bonds is not comprehensive. The first residue in each bond belongs to the SET1Win ligand, whereas the second one belongs to WDR5. Only peptide sequences of the segments that were able to model these interactions are listed below. Entries with multiple distances represent multiple different hydrogen bonds formed by the same residues.

Peptide
Hydrogen Bonds Supplementary Table S6. List of all known noncovalent interactions. These results were obtained in a similar method as that for Table S5. For each interaction, the first residue corresponds to the SET1Win ligand, while the second residue corresponds to WDR5. The cut-off radii for the ionic and cation-pi interactions were 6 Å. Also, the cut-off radii for the hydrophobic and aromatic-aromatic interactions were 5 and 7 Å, respectively.

Location of surface WDR5 mutations within the A and B pockets
Supplementary Fig. S2. Cartoon illustrating the location of key residues present in the A and B pockets of the WDR5 protein.
S-11  *This data are from Imran and co-workers (2021). 2 **In this case, kon was in the order of 10 4 M -1 s -1 assuming that the association process is in the range of values determined with the other SET1Win ligands. ***NO stands for "Not Observed."

Kinetic rate constants of dissociation of WDR5 mutants with SET1Win ligands using BLI measurements.
Supplementary Table S8. Kinetic rate constants of dissociation, koff, of WDR5 mutants with the SET1Win ligands using BLI measurements. koff values were provided in 10 -3 s -1 . Numbers represent mean ± s.d. determined from three independent experimental observations. D92N did not show any measurable binding interactions using BLI, so it was not included in this  Supplementary Fig. S4: Normalized association rate constants of the WDR5-SET1Win interactions using BLI sensorgrams. The kon values for each SET1Win ligand's interaction with WDR5 mutants have been divided by the kon of that SET1Win ligand's interaction with the native WDR5 protein. ND stands for "Not Determined." Interaction between F133L and MLL4 was detectable, but not quantifiable, using a BLI measurement.

Normalized kinetic rate constants of association of WDR5 mutants with SET1Win ligands using BLI measurements
S-14

Equilibrium dissociation constants of WDR5 mutants with SET1Win ligands using BLI measurements.
Supplementary Table S9. Equilibrium dissociation constants, KD-BLI, of the WDR5 mutants with the SET1Win ligands determined from BLI measurements. KD-BLI values are provided in nM. Numbers represent mean ± s.d. determined from three independent experimental determinations. D92N did not show any measurable binding interactions using the BLI, so it was not included in this table.
MLL1Win ≳100,000* ≳ 100,000* ≳ 100,000* ≳ 100,000* NO** ≳ 100,000* ≳ 100,000* # This data are from Imran and co-workers (2021). 2 *This upper-limit value for the detection of KD-BLI results from dividing the upper-limit value of the detection of koff by the value of the kon approximation. **NO stands for "Not Observed." S-15

Structural information on the effect of the S175L mutation.
Supplementary Fig. S5. The effect of the S175L mutation on the SETd1AWin-S175L interaction. The figure shows the effect of the S175L mutation on neighboring residues. It shows superimposed structures from PDB 4es0, 4ewr and 4erz. SETd1AWin is marked in cyan, while MLL4Win is marked in light blue. The red circles show the steric clashes created by replacing Ser-175 with Leu-175 (green). Superimposed Tyr-191 side chains from the three PDB files are shown. SETd1AWin was used, instead of SETd1BWin, to show the B-pocket interactions, because the P6 residue in the SETd1BWin structure is disordered.  Supplementary Fig. S6. Steady-state FP anisotropy curves for WDR5-SET1Win ligand interactions. The N terminus of the SET1Win ligands was tagged with Sulforhodamine B, whereas the C terminus was amidated. The final concentration of the labeled SET1Win ligands in each well was 10 nM. Each SET1Win ligand -WDR5 run involved a 2-fold serial dilution of WDR5 over 24 wells. Three independent experiments were conducted to obtain the dose response, which was fitted using a four-parameter logistic model to get the KD.

Quantitative comparisons of affinity data acquired with BLI and FP.
Supplementary